Question

a.

Which of the following is true?

Group of answer choices

Additional variables can add noise to the model that slightly increases R-squared

All the options are true

You can use multiple independent variables to predict a dependent variable

The R-Squared value is a measure of how good the model is.

b.This term refers to when two predictor variables are highly correlated with each other and so the effect of the variables on the dependent response is questionable.

Answer 1

a. Which of the following is true?

1) Additional variables can add noise to the model that slightly increases R-squared:- TRUE

As we increase number of variable R squares get increases. if the added variable is insignificant then also R squared increases.

2) You can use multiple independent variables to predict a dependent variable:- TRUE

Yes using multiple linear regression.

3) The R-Squared value is a measure of how good the model is: TRUE

R squared tells us how much variation in the dependent variable is explained by the model.

as the variance is near to 1 we can say that model is a good fit.it is low then the model is not a good fit.

Answer:- All the options are true

b.This term refers to when two predictor variables are highly correlated with each other and so the effect of the variables on the dependent response is questionable.

Answer:- Multicollinearity

Multicollinearity refers to a situation in which two or more explanatory variables in a multiple regression model are highly linearly related.

Multicollinearity is a phenomenon in which one predictor variable in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy.